1. Field of the Invention
The present invention relates to a learning apparatus for a pattern detector which detects a specific pattern from input data by classifications using a plurality of classifiers, a learning method, and a computer-readable storage medium.
2. Description of the Related Art
Conventionally, pattern detection methods each for detecting a specific pattern (for example, a character or human face) from input data have been proposed. Also, many methods which aim to speed up processing and improve detection precision have been proposed.
In association with such techniques, [Viola & Jones (2001) “Rapid Object Detection using a Boosted Cascade of Simple Features”, Computer Vision and Pattern Recognition. (to be referred to as reference 1 hereinafter)] has proposed a technique which attains pattern detection by cascade-connecting a large number of weak classifiers which can make arithmetic operations within a short period of time, and by combining a group learning (ensemble learning) method represented by Adaboost. A weak classifier of reference 1 includes a filter which is called a rectangular filter based on a Haar base, and calculates a difference between average luminance values of regions in a rectangle. Using, the average value difference in a rectangular region calculated by this rectangular filter is used as a feature amount, and is compared with a predetermined threshold, thus determining whether or not an object is a pattern to be detected.
A final pattern detector is configured by combining a large number of weak classifiers, as described above. In reference 1, the weighted sum total of a plurality of weak classifiers is output. This configuration method uses a learning algorithm called ensemble learning (group learning). A typical algorithm of ensemble learning is Adaboost. Adaboost sets weights for samples for learning, and when learning of one weak classifier is completed, learning of the next weak classifier is started. In learning of this next weak classifier, the weights of data are sequentially updated so that the weight of a sample that is poorly classified by the previous weak classifier is set to be large. For each weak classifier, a degree of confidence indicating its classification performance is defined. This degree of confidence is decided based on, for example, an error ratio with respect to samples for learning during a learning phase.
When building the aforementioned pattern detector comprised of a large number of weak classifiers, a very large amount of time is required for learning. As described above, learning for each, respective weak classifier includes a process of making pattern detections with respect to samples for learning, and evaluating its detection performance. In order to build a high-performance detector, weak classifiers are required to have complicated expressions, and the number of weak classifier candidates to be evaluated becomes very large. Therefore, the number of repetitions of the above process also becomes very large.
To solve this problem, Japanese Patent Laid-Open No. 2005-44330 (to be referred to as reference 2 hereinafter) has proposed the following method. Weak classifier candidates, which are prepared in advance, are evaluated, some candidates that exhibit high performance are updated by a predetermined method, and a candidate which exhibits the highest performance among all is adopted as a weak classifier.
Also, [C. Huang, H. Ai, Y. Li, S. Lao (2006) “Learning Sparse Features in Granular Space for Multi-View Face Detection”, Proceedings of the IEEE International Conference of Automatic Face and Gesture Recognition, pp. 401-406. (to be referred to as reference 3 hereinafter)] gives a Haar base as an initial candidate of a weak classifier. To this Haar base, specified expansion operators of three types (refine, add, remove) are defined, and a weak classifier is expanded by applying them to the weak classifier. That is, reference 3 has proposed a method which adopts a function that considers the detection performance and complexity of a weak classifier as an evaluation function, and searches for a weak classifier which exhibits comprehensively high performance in consideration of not only the detection performance but also a detection time.